To satisfy the increasing demands of high-speed transmission, high-efficiency computing, and real-time communications in the high-dynamic and heterogeneous networks, the Contact Plan Design(CPD) has attracted continuo...To satisfy the increasing demands of high-speed transmission, high-efficiency computing, and real-time communications in the high-dynamic and heterogeneous networks, the Contact Plan Design(CPD) has attracted continuous attention in recent years, especially for the spatial-node-based Internet of Everything(IoE). In this paper, we study the NP-hardness of contact scheduling and the attenuation of atmospheric precipitation in the spatial-node-based IoE. Two heuristic computing methods for contact plan design are proposed by comprehensively considering the time-varying topology, the intermittent connectivity, and the adaptive transmission in different weather conditions, which are named Contact Plan Design-Particle Swarm Optimization(CPD-PSO) and Contact Plan Design-Greedy algorithm with the Minimum Delivery Time(CPD-GMDT) separately. For the population-based algorithm, CPD-PSO not only solves the CPD problem with a limited-resource condition, but also dynamically adjusts the search scope to ensure the continuous searching capability of the algorithm. For the CPD-GMDT that makes CP decisions based on the current state, the algorithm uses the idea of greedy algorithm to schedule Satellite-Platform Links(SPLs) and Inter Satellite Links(ISLs) respectively using the strategies of optimal matching and load balancing. The simulation results show that the proposed CPD-PSO outperforms Contact Plan Design-Genetic Algorithm(CPD-GA) in terms of fitness and delivery time, and CPD-GMDT presents better overall delay than Fair Contact Plan(FCP).展开更多
Principal component analysis(PCA) is fundamental in many pattern recognition applications.Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization(L1-PCA...Principal component analysis(PCA) is fundamental in many pattern recognition applications.Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization(L1-PCA-REM) since conventional L2-norm based PCA(L2-PCA) is sensitive to outliers.Recently,the variance maximization formulation of PCA with L1-norm(L1-PCA-VM) has been proposed,where new greedy and nongreedy solutions are developed.Armed with the gradient ascent perspective for optimization,we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation,which are verified by experiments on synthetic and real-world datasets.展开更多
The orthogonal multi-matching pursuit （OMMP） is a natural extension of the orthogo- nal matching pursuit （OMP）. We denote the OMMP with the parameter M as OMMP（M） where M ≥ 1 is an integer. The main difference ...The orthogonal multi-matching pursuit （OMMP） is a natural extension of the orthogo- nal matching pursuit （OMP）. We denote the OMMP with the parameter M as OMMP（M） where M ≥ 1 is an integer. The main difference between OMP and OMMP（M） is that OMMP（M） selects M atoms per iteration, while OMP only adds one atom to the op- timal atom set. In this paper, we study the performance of orthogonal multi-matching pursuit under RIP. In particular, we show that, when the measurement matrix A satisfies （25s, 1/10）-RIP, OMMP（M0） with M0 = 12 can recover s-sparse signals within s itera- tions. We furthermore prove that OMMP（M） can recover s-sparse signals within O（s/M） iterations for a large class of M.展开更多
基金jointly supported by the National Natural Science Foundation in China (61601075, 61671092, 61771120, 61801105)the Fundamental Research Funds for the Central University (N171602002)the Natural Science Foundation Project of CQ CSTC (cstc2016jcyjA0174)
文摘To satisfy the increasing demands of high-speed transmission, high-efficiency computing, and real-time communications in the high-dynamic and heterogeneous networks, the Contact Plan Design(CPD) has attracted continuous attention in recent years, especially for the spatial-node-based Internet of Everything(IoE). In this paper, we study the NP-hardness of contact scheduling and the attenuation of atmospheric precipitation in the spatial-node-based IoE. Two heuristic computing methods for contact plan design are proposed by comprehensively considering the time-varying topology, the intermittent connectivity, and the adaptive transmission in different weather conditions, which are named Contact Plan Design-Particle Swarm Optimization(CPD-PSO) and Contact Plan Design-Greedy algorithm with the Minimum Delivery Time(CPD-GMDT) separately. For the population-based algorithm, CPD-PSO not only solves the CPD problem with a limited-resource condition, but also dynamically adjusts the search scope to ensure the continuous searching capability of the algorithm. For the CPD-GMDT that makes CP decisions based on the current state, the algorithm uses the idea of greedy algorithm to schedule Satellite-Platform Links(SPLs) and Inter Satellite Links(ISLs) respectively using the strategies of optimal matching and load balancing. The simulation results show that the proposed CPD-PSO outperforms Contact Plan Design-Genetic Algorithm(CPD-GA) in terms of fitness and delivery time, and CPD-GMDT presents better overall delay than Fair Contact Plan(FCP).
基金Project supported by the National Natural Science Foundation of China (Nos. 61071131 and 61271388)the Beijing Natural Science Foundation (No. 4122040)+1 种基金the Research Project of Tsinghua University (No. 2012Z01011)the United Technologies Research Center (UTRC)
文摘Principal component analysis(PCA) is fundamental in many pattern recognition applications.Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization(L1-PCA-REM) since conventional L2-norm based PCA(L2-PCA) is sensitive to outliers.Recently,the variance maximization formulation of PCA with L1-norm(L1-PCA-VM) has been proposed,where new greedy and nongreedy solutions are developed.Armed with the gradient ascent perspective for optimization,we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation,which are verified by experiments on synthetic and real-world datasets.
文摘The orthogonal multi-matching pursuit （OMMP） is a natural extension of the orthogo- nal matching pursuit （OMP）. We denote the OMMP with the parameter M as OMMP（M） where M ≥ 1 is an integer. The main difference between OMP and OMMP（M） is that OMMP（M） selects M atoms per iteration, while OMP only adds one atom to the op- timal atom set. In this paper, we study the performance of orthogonal multi-matching pursuit under RIP. In particular, we show that, when the measurement matrix A satisfies （25s, 1/10）-RIP, OMMP（M0） with M0 = 12 can recover s-sparse signals within s itera- tions. We furthermore prove that OMMP（M） can recover s-sparse signals within O（s/M） iterations for a large class of M.
